It is commonly accepted that machine translation is a more complex task than part of speech tagging. But how much more complex? In this paper we make an attempt to develop a general framework and methodology for computing the informational and/or processing complexity of NLP applications and tasks. We define a universal framework akin to a Turning Machine that attempts to fit (most) NLP tasks into one paradigm. We calculate the complexities of various NLP tasks using measures of Shannon Entropy, and compare `simple' ones such as part of speech tagging to `complex' ones such as machine translation. This paper provides a first, though far from perfect, attempt to quantify NLP tasks under a uniform paradigm. We point out current deficiencies and suggest some avenues for fruitful research.
Classification of Natural Language Processing Techniques for Requirements Engineering
University of Manchester（曼彻斯特大学）；Al-Imam Mohammed ibn Saud Islamic University（伊玛目·穆罕默德·本·沙特伊斯兰大学）；Consiglio Nazionale delle Ricerche（意大利国家研究委员会）；Higher Colleges of Technology（高等技术学院）
A Hybrid, Multi-Layered Pipeline for Phishing and Threat Classification: Independently Validated URL and NLP Engines with a Calibrated Multi-Channel Fusion Stage
Department of Communications and Information Engineering（通讯与信息工程系）；Zewail City of Science and Technology（泽瓦尔科学与技术城）
